Implementing an Improved Test of Matrix Rank in Stata

08/01/2021
by   Qihui Chen, et al.
0

We develop a Stata command, bootranktest, for implementing the matrix rank test of Chen and Fang (2019) in linear instrumental variable regression models. Existing rank tests employ critical values that may be too small, and hence may not even be first order valid in the sense that they may fail to control the Type I error. By appealing to the bootstrap, they devise a test that overcomes the deficiency of existing tests. The command bootranktest implements the two-step version of their test, and also the analytic version if chosen. The command also accommodates data with temporal and cluster dependence.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/06/2018

Improved Inference on the Rank of a Matrix

This paper develops a general framework for conducting inference on the ...
research
11/13/2020

The Safe Log Rank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification

We introduce the safe log rank test, a version of the log rank test that...
research
04/23/2020

Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models

This paper provides general expression for Bartlett and Bartlett-type co...
research
05/08/2020

How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?

We develop theoretical finite-sample results concerning the size of wild...
research
03/22/2021

Block Length Choice for the Bootstrap of Dependent Panel Data – a Comment on Choi and Shin (2020)

Choi and Shin (2020) have constructed a bootstrap-based test for change-...
research
12/19/2017

Bayesian Latent-Normal Inference for the Rank Sum Test, the Signed Rank Test, and Spearman's ρ

Bayesian inference for rank-order problems is frustrated by the absence ...
research
02/23/2021

An Aligned Rank Transform Procedure for Multifactor Contrast Tests

Data from multifactor HCI experiments often violates the normality assum...

Please sign up or login with your details

Forgot password? Click here to reset